Techniques (Reference number is mentioned) | Segmentation | Feature extraction | Features | Classification | Advantages | Limitations | Accuracy |
[14] | Color segmentations | DWT + PCA | Features are extracted from DWT which are reduced for better accuracy. | kNN | It requires less computational time and memory. | DWT requires huge capacity and is computationally more expensive. | 97.5% |
[17] | Normalized Otsu thresholding | GLCM+ PCA | Mean, standard deviation, variance, entropy, contrast/inertia, homogeneity, energy, correlation, area, perimeter, diameter, asymmetry index, circularity index, fractal dimension, compactness index | DLNN (Deep learning NN), SVM-Adaboost | This is a CAD system that runs with lower computational time with higher accuracy. | Hybrid segmentation is needed to enhance system performance. | 93% |
[18] | - | GLCM + LBP | Energy, entropy, contrast, homogeneity, and LBP array features. | SVM | System performances are computed both qualitatively and quantitatively. | The segmentation algorithm is undefined and to boost up system performance NN based classification is required. | 90.32 |
[19] | Grab Cut algorithm | Histogram + ABCD rule | Features are the area of the lesion, perimeter of a lesion, eccentricity, mean, standard deviation, L1 norm, L2 norm angle of lesion, major and minor axis of the lesion from the segmented image. | SVM | It is easy to access and use due to its Smartphone embedded applications. | An improper segmentation algorithm is used which degrades system performance. | - |
[22] | Threshold-based Adaptive Snake (AS) approach | ABCD rule + Epiluminescence microscopy (ELM) criteria algorithm | Features extracted in ABCD rule with ELM criteria. | GA + SVM with Radial Basis Function (RBF) | GA reduces the dimensions and also defines the most discriminating subsets of features to boost system performance. | An efficient and reliable segmentation algorithm is required. | 88% |
[24] | Otsu thresholding | GLCM + ABCD rule + PCA | GLCM features are Energy, correlation, homogeneity, and contrast features. The best 5 features with maximum efficiency as follows: TDS, mean, standard deviation, energy, and contrast respectively. | SVM | The computational complexity is relatively lower than others. | Hybrid segmentation and NN-based classification are needed. | 92.10% |